NAME

       v.qcount  - Indices for quadrat counts of vector point lists.

KEYWORDS

       vector, statistics, point pattern

SYNOPSIS

       v.qcount
       v.qcount --help
       v.qcount  [-g]  input=name  [layer=string]   [output=name]  nquadrats=integer radius=float
       [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]

   Flags:
       -g
           Print results in shell script style

       --overwrite
           Allow output files to overwrite existing files

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       input=name [required]
           Name of input vector map
           Or data source for direct OGR access

       layer=string
           Layer number or name (’-1’ for all layers)
           A single vector map  can  be  connected  to  multiple  database  tables.  This  number
           determines  which  table  to  use.  When used with direct OGR access this is the layer
           name.
           Default: -1

       output=name
           Name for output quadrat centers map (number of points is written as category)

       nquadrats=integer [required]
           Number of quadrats

       radius=float [required]
           Quadrat radius

DESCRIPTION

       v.qcount computes six different quadrat count statistics that provide  a  measure  of  how
       much an user defined point pattern departs from a complete spatial random point pattern.

       Points are distributed following a complete spatial randomness (CSR) pattern if events are
       equally likely to occur anywhere within an area. There are two types departure from a CSR:
       regularity  and  clustering. Figure 1 gives an example of a complete random, regular and a
       clustered pattern.
       Figure 1: Realization of two-dimensional Poisson processes of 50 points on the unit square
       exhibiting (a) complete spatial randomness, (b) regularity, and (c) clustering.

       Various  indices  and  statistics  measure  departure  from  CSR.  The  v.qcount  function
       implements six different quadrat count indices that are described  in  Cressie  (1991;  p.
       590-591)[1] and in Ripley (1981; p. 102-106)[2] and summarized in Table 1.
       Table  1:  Indices  for Quadrat Count Data. Adapted from Cressie [1], this table shows the
       statistics computed for the quadrats in Figure 2.

       These indices are computed as follows: v.qcount chooses  nquadrads  circular  quadrats  of
       radius radius such that they are completely within the bounds of the current region and no
       two quadrats overlap.  The number of points falling within each quadrat  are  counted  and
       indices  are calculated to estimate the departure of point locations from complete spatial
       randomness. This is illustrated in Figure 2.
       Figure 2: Randomly placed quadrats (n = 100) with 584 sample points.

       The number of points is written as category to the output map (and  not  to  an  attribute
       table).

NOTES

       This  program  may  not  work  properly  with lat-long data. It uses hypot() in two files:
       count.c and findquads.c.

KNOWN ISSUES

       Timestamp not working for header part of counts output. (2000-10-28)

REFERENCES

       General references include:

       [1] Noel A. C. Cressie. Statistics for Spatial Data.   Wiley  Series  in  Probability  and
       Mathematical Statistics. John Wiley & Sons, New York, NY, 1st edition, 1991.

       [2] Brian D. Ripley. Spatial Statistics.  John Wiley \& Sons, New York, NY, 1981.

       References to the indices include:

       [3] R. A. Fisher, H. G. Thornton, and W. A. Mackenzie.  The accuracy of the plating method
       of estimating the density of bacterial populations.  Annals of Applied Biology, 9:325-359,
       1922.

       [4]  F.  N. David and P. G. Moore. Notes on contagious distributions in plant populations.
       Annals of Botany, 18:47-53, 1954.

       [5] J. B. Douglas.  Clustering and aggregation.  Sankhya B, 37:398-417, 1975.

       [6] M. Lloyd. Mean crowding.  Journal of Animal Ecology, 36:1-30, 1967.

       [7] M. Morista. Measuring the dispersion and analysis of distribution  patterns.  Memoires
       of the Faculty of Science, Kyushu University, Series E.  Biology, 2:215-235, 1959.

       A more detailed background is given in the tutorial:

       [8]  James  Darrell McCauley 1993. Complete Spatial Randomness and Quadrat Methods - GRASS
       Tutorial on v.qcount

SEE ALSO

        v.random, v.distance, v.neighbors, v.perturb

AUTHORS

       James Darrell McCauley
       when he was at: Agricultural Engineering Purdue University

       Modified for GRASS 5.0 by Eric G. Miller (2000-10-28)
       Modified for GRASS 5.7 by R. Blazek (2004-10-14)

SOURCE CODE

       Available at: v.qcount source code (history)

       Accessed: Thursday Aug 01 11:31:15 2024

       Main index | Vector index | Topics index | Keywords index | Graphical index | Full index

       © 2003-2024 GRASS Development Team, GRASS GIS 8.4.0 Reference Manual